Gamification has been shown to encourage contributions of user-generated reviews (word-of-mouth: WOM) in various domains, including travel and leisure related platforms (Foursquare, TripAdvisor), e-commerce (Amazon), and auctions (eBay). WOM contributors write reviews about products/services provided by business venues and WOM consumers read reviews and use them to form attitudes and make purchase decisions. Gamification elements such as points and badges, awarded to WOM contributors for various reasons, and displayed to WOM consumers, have a dual role in WOM context. First, points awarded for user contributions help motivate WOM contributors to increase their participation. Second, badges awarded to users for visiting business venues signal prior experience or competence, and they help determine how WOM consumers perceive WOM contributors and form their judgments based on the reviews. While the first role of gamification (i.e., motivating users) has been widely studied, the impact of WOM presented along with gamification elements on the perceptions and behavior of the target audience, WOM consumers, has not been examined. This is important to businesses that are looking to attract customers. Drawing on social psychology literature, we show that gamification symbols signaling experience that accompany WOM leads to perceptions of positive WOM contributors as more competent. This leads to important changes in behavioral outcomes such as willingness to visit/buy and willingness to recommend the reviewed outlets.
Customers are increasingly utilizing location-based services via mobile devices to engage with retail establishments. The focus of this paper is to identify factors that help to drive venue popularity revealed by location-based services, which then better facilitate companies' operational decisions, such as procurement and staff scheduling. Using data collected from Foursquare and Yelp, we build, evaluate, and compare a wide variety of machine learning methods including deep learning models with varying characteristics and degrees of sophistication. First, we find that support vector regression is the best performing model compared to other complex predictive algorithms. Second, we apply SHAP (Shapley Additive exPlanations) to quantify the contribution from each business feature at both the global and local levels. The global interpretability results show that customer loyalty, the agglomeration effect, and the word-of-mouth effect are the top three drivers of venue popularity. Furthermore, the local interpretability analysis reveals that the contributions of business features vary, both quantitatively and directionally. Our findings are robust with respect to different popularity measures, training and testing periods, and prediction horizons. These findings extend our knowledge of location-based services by demonstrating their potential to play a prominent role in attracting consumer engagement and boosting venue popularity. Managers can make better operational decisions such as procurement and staff scheduling based on these more accurate venue popularity prediction methods. Furthermore, this study also highlights the importance of model interpretability which enhances the ability of managers to more effectively utilize machine learning models for effective decision-making.
Spotlight Gamification is an emerging digital strategy to engage users across different business domains. Gamification, defined as using game-design elements in nongaming contexts, shows great potential across domains such as education, business, and health. The significance of gamification is highlighted by a $7.17 billion global market in 2019 and is projected to reach more than $40 billion by 2024. This study examines two popular badges and leaderboards that utilize self-determination and social-comparison mechanisms to promote user engagement. We conduct a randomized field experiment (A/B testing) to quantify these effects in one of the largest shopping malls in Asia and further contrast the two games against coupons regarding various shopping outcomes. Quantitatively, badging and leaderboarding promote sales by 21.5% and 22.5% in the treatment period, respectively, whereas couponing delivers a more potent effect of 31.7%. In the posttreatment period, the gamification impacts remain significant, whereas the influence of couponing fades out. Besides, the additional analyses document the salient heterogeneous treatment effects across gender, age, and income. We also zoom in on the contrast between badges and leaderboards, showing that badging is a balanced tool for attracting the general public and leaderboarding is a double-edged sword that could encourage self-reinforcing or self-banishing. Finally, gamification encourages consumers to do more exploration, leading to significant increases in sales.
Understanding consumers’ engagement and subsequent content consumption behavior in the mobile context is critical to mobile app providers. In this paper, we develop a Hidden Markov Model (HMM) to capture the dynamics of users’ engagement states and consumption decisions on the number of books/chapters read and the amount of money spent. Our method allows us to simultaneously capture three interdependent usage behaviors using a single integrated model and identify the impact of content loading time and previous reading behavior on users’ engagement dynamics and content consumption. We calibrate the model using a tap stream data set of individual users’ reading activities on a mobile app. Our analysis reveals three distinct engagement states, a low state with inactive users, a medium state with users sampling books, and a high state with users reading intensively. Furthermore, we find that content loading time has higher negative impacts on high‐state users in state transitioning than medium‐state users. In contrast, the days that elapsed since the last visit has a similar negative impact on the users in the high and medium states. The effect of usage frequency on users in state transitioning is always positive. We have also identified the weekend effect and social influence on users’ reading outcomes. Finally, our simulations quantify the shortened content loading time and the days elapsed since the last visit on users’ engagement dynamics and content consumption decisions, which generate important managerial implications for app providers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.